Overview

Brought to you by YData

Dataset statistics

Number of variables60
Number of observations125
Missing cells53
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory481.1 B

Variable types

Numeric10
Categorical38
Text12

Alerts

eating_changes_coded is highly overall correlated with eating_changes_coded1High correlation
eating_changes_coded1 is highly overall correlated with eating_changes_codedHigh correlation
employment is highly overall correlated with weightHigh correlation
healthy_feeling is highly overall correlated with life_rewardingHigh correlation
indian_food is highly overall correlated with persian_food and 1 other fieldsHigh correlation
life_rewarding is highly overall correlated with healthy_feelingHigh correlation
persian_food is highly overall correlated with indian_foodHigh correlation
thai_food is highly overall correlated with indian_foodHigh correlation
weight is highly overall correlated with employmentHigh correlation
fries is highly imbalanced (57.0%) Imbalance
comfort_food_reasons has 2 (1.6%) missing values Missing
cook has 3 (2.4%) missing values Missing
drink has 2 (1.6%) missing values Missing
eating_changes has 3 (2.4%) missing values Missing
employment has 9 (7.2%) missing values Missing
father_profession has 3 (2.4%) missing values Missing
fav_cuisine has 2 (1.6%) missing values Missing
fav_food has 2 (1.6%) missing values Missing
meals_dinner_friend has 3 (2.4%) missing values Missing
mother_education has 3 (2.4%) missing values Missing
mother_profession has 2 (1.6%) missing values Missing
sports has 2 (1.6%) missing values Missing
weight has 2 (1.6%) missing values Missing
fav_cuisine_coded has 6 (4.8%) zeros Zeros

Reproduction

Analysis started2025-04-10 17:13:10.784309
Analysis finished2025-04-10 17:13:22.367284
Duration11.58 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

GPA
Real number (ℝ)

Distinct37
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4186529
Minimum2.2
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:22.422281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile2.8
Q13.2
median3.5
Q33.7
95-th percentile3.9
Maximum4
Range1.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.38365649
Coefficient of variation (CV)0.11222446
Kurtosis0.43658389
Mean3.4186529
Median Absolute Deviation (MAD)0.27
Skewness-0.77246485
Sum427.33161
Variance0.1471923
MonotonicityNot monotonic
2025-04-10T14:13:22.525798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.5 13
 
10.4%
3 11
 
8.8%
3.2 10
 
8.0%
3.7 10
 
8.0%
3.3 9
 
7.2%
3.4 9
 
7.2%
3.9 7
 
5.6%
3.6 7
 
5.6%
3.8 6
 
4.8%
2.8 5
 
4.0%
Other values (27) 38
30.4%
ValueCountFrequency (%)
2.2 1
 
0.8%
2.25 1
 
0.8%
2.4 1
 
0.8%
2.6 2
 
1.6%
2.71 1
 
0.8%
2.8 5
4.0%
2.9 2
 
1.6%
3 11
8.8%
3.1 3
 
2.4%
3.2 10
8.0%
ValueCountFrequency (%)
4 4
3.2%
3.92 1
 
0.8%
3.904 1
 
0.8%
3.9 7
5.6%
3.89 1
 
0.8%
3.882 1
 
0.8%
3.87 1
 
0.8%
3.83 2
 
1.6%
3.8 6
4.8%
3.79 1
 
0.8%

Gender
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
76 
2
49 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Length

2025-04-10T14:13:22.624314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:22.697832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring characters

ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

breakfast
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
111 
2
14 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Length

2025-04-10T14:13:22.777350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:22.849352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring characters

ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

calories_chicken
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
610
59 
720
32 
430
25 
265

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row430
2nd row610
3rd row720
4th row430
5th row720

Common Values

ValueCountFrequency (%)
610 59
47.2%
720 32
25.6%
430 25
20.0%
265 9
 
7.2%

Length

2025-04-10T14:13:22.926862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:23.005379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
610 59
47.2%
720 32
25.6%
430 25
20.0%
265 9
 
7.2%

Most occurring characters

ValueCountFrequency (%)
0 116
30.9%
6 68
18.1%
1 59
15.7%
2 41
 
10.9%
7 32
 
8.5%
4 25
 
6.7%
3 25
 
6.7%
5 9
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116
30.9%
6 68
18.1%
1 59
15.7%
2 41
 
10.9%
7 32
 
8.5%
4 25
 
6.7%
3 25
 
6.7%
5 9
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116
30.9%
6 68
18.1%
1 59
15.7%
2 41
 
10.9%
7 32
 
8.5%
4 25
 
6.7%
3 25
 
6.7%
5 9
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116
30.9%
6 68
18.1%
1 59
15.7%
2 41
 
10.9%
7 32
 
8.5%
4 25
 
6.7%
3 25
 
6.7%
5 9
 
2.4%

calories_day
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3.0
63 
4.0
23 
2.0
20 
0.0
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row4.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 63
50.4%
4.0 23
 
18.4%
2.0 20
 
16.0%
0.0 19
 
15.2%

Length

2025-04-10T14:13:23.091895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:23.169409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.0 63
50.4%
4.0 23
 
18.4%
2.0 20
 
16.0%
0.0 19
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

calories_scone
Categorical

Distinct3
Distinct (%)2.4%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
420.0
79 
980.0
23 
315.0
22 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters620
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row315.0
2nd row420.0
3rd row420.0
4th row420.0
5th row420.0

Common Values

ValueCountFrequency (%)
420.0 79
63.2%
980.0 23
 
18.4%
315.0 22
 
17.6%
(Missing) 1
 
0.8%

Length

2025-04-10T14:13:23.264925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:23.342929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
420.0 79
63.7%
980.0 23
 
18.5%
315.0 22
 
17.7%

Most occurring characters

ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

coffee
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
94 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Length

2025-04-10T14:13:23.424445image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:23.495959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring characters

ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%
Distinct124
Distinct (%)100.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2025-04-10T14:13:23.594473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length97
Median length48
Mean length34.766129
Min length4

Characters and Unicode

Total characters4311
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st rownone
2nd rowchocolate, chips, ice cream
3rd rowfrozen yogurt, pizza, fast food
4th rowPizza, Mac and cheese, ice cream
5th rowIce cream, chocolate, chips
ValueCountFrequency (%)
ice 50
 
7.9%
cream 47
 
7.4%
and 40
 
6.3%
pizza 40
 
6.3%
chocolate 35
 
5.5%
chips 34
 
5.3%
cheese 22
 
3.5%
cookies 19
 
3.0%
mac 17
 
2.7%
chicken 14
 
2.2%
Other values (169) 318
50.0%
2025-04-10T14:13:23.831372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4311
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4311
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4311
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

comfort_food_reasons
Text

Missing 

Distinct106
Distinct (%)86.2%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2025-04-10T14:13:23.996398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length137
Median length59
Mean length26.902439
Min length4

Characters and Unicode

Total characters3309
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)78.9%

Sample

1st rowwe dont have comfort
2nd rowStress, bored, anger
3rd rowstress, sadness
4th rowBoredom
5th rowStress, boredom, cravings
ValueCountFrequency (%)
boredom 74
 
14.6%
sadness 42
 
8.3%
stress 33
 
6.5%
and 26
 
5.1%
i 19
 
3.7%
when 12
 
2.4%
anger 10
 
2.0%
comfort 10
 
2.0%
happiness 9
 
1.8%
bored 9
 
1.8%
Other values (158) 264
52.0%
2025-04-10T14:13:24.295949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

cook
Categorical

Missing 

Distinct5
Distinct (%)4.1%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
3.0
49 
2.0
34 
4.0
18 
1.0
13 
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters366
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 49
39.2%
2.0 34
27.2%
4.0 18
 
14.4%
1.0 13
 
10.4%
5.0 8
 
6.4%
(Missing) 3
 
2.4%

Length

2025-04-10T14:13:24.405462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:24.485976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.0 49
40.2%
2.0 34
27.9%
4.0 18
 
14.8%
1.0 13
 
10.7%
5.0 8
 
6.6%

Most occurring characters

ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%
Distinct8
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.688
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:24.564492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9109869
Coefficient of variation (CV)0.71093263
Kurtosis3.4213848
Mean2.688
Median Absolute Deviation (MAD)1
Skewness1.922052
Sum336
Variance3.651871
MonotonicityNot monotonic
2025-04-10T14:13:24.646497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 53
42.4%
1 28
22.4%
3 23
18.4%
5 7
 
5.6%
9 5
 
4.0%
7 5
 
4.0%
4 3
 
2.4%
6 1
 
0.8%
ValueCountFrequency (%)
1 28
22.4%
2 53
42.4%
3 23
18.4%
4 3
 
2.4%
5 7
 
5.6%
6 1
 
0.8%
7 5
 
4.0%
9 5
 
4.0%
ValueCountFrequency (%)
9 5
 
4.0%
7 5
 
4.0%
6 1
 
0.8%
5 7
 
5.6%
4 3
 
2.4%
3 23
18.4%
2 53
42.4%
1 28
22.4%

cuisine
Real number (ℝ)

Distinct6
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.016
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:24.730005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.827143
Coefficient of variation (CV)0.90632094
Kurtosis0.72852936
Mean2.016
Median Absolute Deviation (MAD)0
Skewness1.5641079
Sum252
Variance3.3384516
MonotonicityNot monotonic
2025-04-10T14:13:24.807519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 86
68.8%
6 19
 
15.2%
2 13
 
10.4%
3 3
 
2.4%
4 3
 
2.4%
5 1
 
0.8%
ValueCountFrequency (%)
1 86
68.8%
2 13
 
10.4%
3 3
 
2.4%
4 3
 
2.4%
5 1
 
0.8%
6 19
 
15.2%
ValueCountFrequency (%)
6 19
 
15.2%
5 1
 
0.8%
4 3
 
2.4%
3 3
 
2.4%
2 13
 
10.4%
1 86
68.8%
Distinct124
Distinct (%)100.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2025-04-10T14:13:24.956044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length262
Median length110.5
Mean length87.669355
Min length6

Characters and Unicode

Total characters10871
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st roweat good and exercise
2nd rowI eat about three times a day with some snacks. I try to eat healthy but it doesn't always work out that- sometimes eat fast food and mainly eat at Laker/ Egan
3rd rowtoast and fruit for breakfast, salad for lunch, usually grilled chicken and veggies (or some variation) for dinner
4th rowCollege diet, cheap and easy foods most nights. Weekends traditionally, cook better homemade meals
5th rowI try to eat healthy but often struggle because of living on campus. I still try to keep the choices I do make balanced with fruits and vegetables and limit the sweats.
ValueCountFrequency (%)
i 151
 
7.3%
and 111
 
5.4%
eat 101
 
4.9%
a 79
 
3.8%
of 57
 
2.8%
to 47
 
2.3%
healthy 39
 
1.9%
diet 34
 
1.6%
vegetables 30
 
1.5%
try 28
 
1.4%
Other values (447) 1386
67.2%
2025-04-10T14:13:25.238661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2007
18.5%
e 992
 
9.1%
a 920
 
8.5%
t 830
 
7.6%
o 609
 
5.6%
s 536
 
4.9%
n 492
 
4.5%
i 488
 
4.5%
r 441
 
4.1%
l 437
 
4.0%
Other values (52) 3119
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2007
18.5%
e 992
 
9.1%
a 920
 
8.5%
t 830
 
7.6%
o 609
 
5.6%
s 536
 
4.9%
n 492
 
4.5%
i 488
 
4.5%
r 441
 
4.1%
l 437
 
4.0%
Other values (52) 3119
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2007
18.5%
e 992
 
9.1%
a 920
 
8.5%
t 830
 
7.6%
o 609
 
5.6%
s 536
 
4.9%
n 492
 
4.5%
i 488
 
4.5%
r 441
 
4.1%
l 437
 
4.0%
Other values (52) 3119
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2007
18.5%
e 992
 
9.1%
a 920
 
8.5%
t 830
 
7.6%
o 609
 
5.6%
s 536
 
4.9%
n 492
 
4.5%
i 488
 
4.5%
r 441
 
4.1%
l 437
 
4.0%
Other values (52) 3119
28.7%
Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
60 
1
50 
3
10 
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Length

2025-04-10T14:13:25.510228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:25.585347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

drink
Categorical

Missing 

Distinct2
Distinct (%)1.6%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2.0
69 
1.0
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 69
55.2%
1.0 54
43.2%
(Missing) 2
 
1.6%

Length

2025-04-10T14:13:25.668862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:25.737866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 69
56.1%
1.0 54
43.9%

Most occurring characters

ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

eating_changes
Text

Missing 

Distinct121
Distinct (%)99.2%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
2025-04-10T14:13:25.880636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length318
Median length85
Mean length58.434426
Min length4

Characters and Unicode

Total characters7129
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)98.4%

Sample

1st roweat faster
2nd rowI eat out more than usual.
3rd rowsometimes choosing to eat fast food instead of cooking simply for convenience
4th rowAccepting cheap and premade/store bought foods
5th rowI have eaten generally the same foods but I do find myself eating the same food frequently due to what I have found I like from egan and the laker.
ValueCountFrequency (%)
i 122
 
8.7%
eat 58
 
4.1%
more 58
 
4.1%
to 46
 
3.3%
and 43
 
3.1%
have 33
 
2.4%
a 30
 
2.1%
food 26
 
1.9%
less 25
 
1.8%
the 24
 
1.7%
Other values (362) 939
66.9%
2025-04-10T14:13:26.176296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

eating_changes_coded
Categorical

High correlation 

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
75 
2
36 
3
11 
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Length

2025-04-10T14:13:26.281818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:26.359335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

eating_changes_coded1
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.552
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:26.434331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile11
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5477884
Coefficient of variation (CV)0.55970747
Kurtosis1.9073443
Mean4.552
Median Absolute Deviation (MAD)1
Skewness1.554581
Sum569
Variance6.4912258
MonotonicityNot monotonic
2025-04-10T14:13:26.516846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 44
35.2%
5 32
25.6%
2 15
 
12.0%
4 12
 
9.6%
8 5
 
4.0%
11 5
 
4.0%
7 3
 
2.4%
10 3
 
2.4%
12 2
 
1.6%
1 1
 
0.8%
Other values (3) 3
 
2.4%
ValueCountFrequency (%)
1 1
 
0.8%
2 15
 
12.0%
3 44
35.2%
4 12
 
9.6%
5 32
25.6%
6 1
 
0.8%
7 3
 
2.4%
8 5
 
4.0%
9 1
 
0.8%
10 3
 
2.4%
ValueCountFrequency (%)
13 1
 
0.8%
12 2
 
1.6%
11 5
 
4.0%
10 3
 
2.4%
9 1
 
0.8%
8 5
 
4.0%
7 3
 
2.4%
6 1
 
0.8%
5 32
25.6%
4 12
 
9.6%

eating_out
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
60 
3
24 
1
16 
4
13 
5
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Length

2025-04-10T14:13:26.603361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:26.681883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring characters

ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

employment
Categorical

High correlation  Missing 

Distinct3
Distinct (%)2.6%
Missing9
Missing (%)7.2%
Memory size1.1 KiB
2.0
60 
3.0
54 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters348
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 60
48.0%
3.0 54
43.2%
1.0 2
 
1.6%
(Missing) 9
 
7.2%

Length

2025-04-10T14:13:26.774027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:26.848040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 60
51.7%
3.0 54
46.6%
1.0 2
 
1.7%

Most occurring characters

ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

ethnic_food
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
42 
4
36 
3
25 
2
17 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Length

2025-04-10T14:13:26.931072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:27.011601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring characters

ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

exercise
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
57 
2.0
44 
0.0
13 
3.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 57
45.6%
2.0 44
35.2%
0.0 13
 
10.4%
3.0 11
 
8.8%

Length

2025-04-10T14:13:27.107125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:27.190635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 57
45.6%
2.0 44
35.2%
0.0 13
 
10.4%
3.0 11
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

father_education
Categorical

Distinct5
Distinct (%)4.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
4.0
46 
2.0
34 
5.0
28 
3.0
12 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row2.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 46
36.8%
2.0 34
27.2%
5.0 28
22.4%
3.0 12
 
9.6%
1.0 4
 
3.2%
(Missing) 1
 
0.8%

Length

2025-04-10T14:13:27.276150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:27.358658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 46
37.1%
2.0 34
27.4%
5.0 28
22.6%
3.0 12
 
9.7%
1.0 4
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

father_profession
Text

Missing 

Distinct114
Distinct (%)93.4%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
2025-04-10T14:13:27.524186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length23.5
Mean length14.213115
Min length2

Characters and Unicode

Total characters1734
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)86.9%

Sample

1st rowprofesor
2nd rowSelf employed
3rd rowowns business
4th rowMechanic
5th rowIT
ValueCountFrequency (%)
business 11
 
4.6%
manager 8
 
3.3%
of 7
 
2.9%
owner 7
 
2.9%
engineer 7
 
2.9%
driver 5
 
2.1%
company 4
 
1.7%
construction 4
 
1.7%
salesman 4
 
1.7%
retired 3
 
1.2%
Other values (144) 180
75.0%
2025-04-10T14:13:27.789268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

fav_cuisine
Text

Missing 

Distinct60
Distinct (%)48.8%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2025-04-10T14:13:27.926795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length40
Median length38
Mean length9.9918699
Min length4

Characters and Unicode

Total characters1229
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)37.4%

Sample

1st rowArabic cuisine
2nd rowItalian
3rd rowitalian
4th rowTurkish
5th rowItalian
ValueCountFrequency (%)
italian 56
30.8%
mexican 12
 
6.6%
chinese 11
 
6.0%
food 10
 
5.5%
american 10
 
5.5%
cuisine 7
 
3.8%
and 5
 
2.7%
thai 4
 
2.2%
indian 4
 
2.2%
or 4
 
2.2%
Other values (52) 59
32.4%
2025-04-10T14:13:28.162967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

fav_cuisine_coded
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.424
Minimum0
Maximum8
Zeros6
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:28.258486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9479683
Coefficient of variation (CV)0.8036173
Kurtosis0.28586948
Mean2.424
Median Absolute Deviation (MAD)1
Skewness1.0211239
Sum303
Variance3.7945806
MonotonicityNot monotonic
2025-04-10T14:13:28.342492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 59
47.2%
4 22
 
17.6%
5 15
 
12.0%
2 15
 
12.0%
0 6
 
4.8%
8 4
 
3.2%
3 2
 
1.6%
6 1
 
0.8%
7 1
 
0.8%
ValueCountFrequency (%)
0 6
 
4.8%
1 59
47.2%
2 15
 
12.0%
3 2
 
1.6%
4 22
 
17.6%
5 15
 
12.0%
6 1
 
0.8%
7 1
 
0.8%
8 4
 
3.2%
ValueCountFrequency (%)
8 4
 
3.2%
7 1
 
0.8%
6 1
 
0.8%
5 15
 
12.0%
4 22
 
17.6%
3 2
 
1.6%
2 15
 
12.0%
1 59
47.2%
0 6
 
4.8%

fav_food
Categorical

Missing 

Distinct3
Distinct (%)2.4%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
1.0
73 
3.0
38 
2.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 73
58.4%
3.0 38
30.4%
2.0 12
 
9.6%
(Missing) 2
 
1.6%

Length

2025-04-10T14:13:28.435603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:28.510121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 73
59.3%
3.0 38
30.9%
2.0 12
 
9.8%

Most occurring characters

ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%
Distinct114
Distinct (%)91.9%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2025-04-10T14:13:28.633634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length91
Median length40
Mean length25.532258
Min length5

Characters and Unicode

Total characters3166
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)86.3%

Sample

1st rowrice and chicken
2nd rowchicken and biscuits, beef soup, baked beans
3rd rowmac and cheese, pizza, tacos
4th rowBeef stroganoff, tacos, pizza
5th rowPasta, chicken tender, pizza
ValueCountFrequency (%)
chicken 55
 
11.7%
and 46
 
9.8%
pizza 39
 
8.3%
pasta 23
 
4.9%
cheese 21
 
4.5%
spaghetti 18
 
3.8%
mac 14
 
3.0%
steak 13
 
2.8%
potatoes 8
 
1.7%
nuggets 7
 
1.5%
Other values (134) 225
48.0%
2025-04-10T14:13:28.884774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
382
 
12.1%
a 317
 
10.0%
e 302
 
9.5%
i 191
 
6.0%
s 175
 
5.5%
n 172
 
5.4%
t 170
 
5.4%
c 165
 
5.2%
h 133
 
4.2%
, 131
 
4.1%
Other values (43) 1028
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
382
 
12.1%
a 317
 
10.0%
e 302
 
9.5%
i 191
 
6.0%
s 175
 
5.5%
n 172
 
5.4%
t 170
 
5.4%
c 165
 
5.2%
h 133
 
4.2%
, 131
 
4.1%
Other values (43) 1028
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
382
 
12.1%
a 317
 
10.0%
e 302
 
9.5%
i 191
 
6.0%
s 175
 
5.5%
n 172
 
5.4%
t 170
 
5.4%
c 165
 
5.2%
h 133
 
4.2%
, 131
 
4.1%
Other values (43) 1028
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
382
 
12.1%
a 317
 
10.0%
e 302
 
9.5%
i 191
 
6.0%
s 175
 
5.5%
n 172
 
5.4%
t 170
 
5.4%
c 165
 
5.2%
h 133
 
4.2%
, 131
 
4.1%
Other values (43) 1028
32.5%

fries
Categorical

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
114 
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Length

2025-04-10T14:13:28.990819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:29.061882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring characters

ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

fruit_day
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
63 
4
33 
3
24 
2
 
4
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row5
2nd row4
3rd row5
4th row4
5th row4

Common Values

ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Length

2025-04-10T14:13:29.139884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:29.215399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

grade_level
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
37 
2
32 
4
28 
3
28 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Length

2025-04-10T14:13:29.304913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:29.382982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring characters

ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

greek_food
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
41 
3
32 
4
23 
1
15 
2
14 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Length

2025-04-10T14:13:29.466496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:29.546507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring characters

ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

healthy_feeling
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.456
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:29.628013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5856427
Coefficient of variation (CV)0.47390813
Kurtosis-1.1235065
Mean5.456
Median Absolute Deviation (MAD)2
Skewness-0.058284658
Sum682
Variance6.6855484
MonotonicityNot monotonic
2025-04-10T14:13:29.708492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 17
13.6%
7 16
12.8%
5 15
12.0%
3 15
12.0%
4 13
10.4%
2 12
9.6%
6 12
9.6%
9 12
9.6%
1 8
6.4%
10 5
 
4.0%
ValueCountFrequency (%)
1 8
6.4%
2 12
9.6%
3 15
12.0%
4 13
10.4%
5 15
12.0%
6 12
9.6%
7 16
12.8%
8 17
13.6%
9 12
9.6%
10 5
 
4.0%
ValueCountFrequency (%)
10 5
 
4.0%
9 12
9.6%
8 17
13.6%
7 16
12.8%
6 12
9.6%
5 15
12.0%
4 13
10.4%
3 15
12.0%
2 12
9.6%
1 8
6.4%
Distinct124
Distinct (%)100.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2025-04-10T14:13:29.845020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length150
Median length71
Mean length49.967742
Min length5

Characters and Unicode

Total characters6196
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st rowlooks not oily
2nd rowGrains, Veggies, (more of grains and veggies), small protein and fruit with dairy
3rd rowusually includes natural ingredients; nonprocessed food
4th rowFresh fruits& vegetables, organic meats
5th rowA lean protein such as grilled chicken, green vegetables and brown rice or other whole grain
ValueCountFrequency (%)
and 87
 
8.2%
a 71
 
6.7%
of 60
 
5.7%
protein 39
 
3.7%
vegetables 34
 
3.2%
meat 26
 
2.5%
with 26
 
2.5%
veggies 24
 
2.3%
chicken 20
 
1.9%
fruits 19
 
1.8%
Other values (247) 651
61.6%
2025-04-10T14:13:30.153076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
980
15.8%
e 613
 
9.9%
a 489
 
7.9%
t 395
 
6.4%
o 384
 
6.2%
r 328
 
5.3%
i 325
 
5.2%
s 315
 
5.1%
n 310
 
5.0%
l 248
 
4.0%
Other values (50) 1809
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
980
15.8%
e 613
 
9.9%
a 489
 
7.9%
t 395
 
6.4%
o 384
 
6.2%
r 328
 
5.3%
i 325
 
5.2%
s 315
 
5.1%
n 310
 
5.0%
l 248
 
4.0%
Other values (50) 1809
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
980
15.8%
e 613
 
9.9%
a 489
 
7.9%
t 395
 
6.4%
o 384
 
6.2%
r 328
 
5.3%
i 325
 
5.2%
s 315
 
5.1%
n 310
 
5.0%
l 248
 
4.0%
Other values (50) 1809
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
980
15.8%
e 613
 
9.9%
a 489
 
7.9%
t 395
 
6.4%
o 384
 
6.2%
r 328
 
5.3%
i 325
 
5.2%
s 315
 
5.1%
n 310
 
5.0%
l 248
 
4.0%
Other values (50) 1809
29.2%
Distinct124
Distinct (%)100.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2025-04-10T14:13:30.305041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length290
Median length108
Mean length77.895161
Min length8

Characters and Unicode

Total characters9659
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st rowbeing healthy
2nd rowTry to eat 5-6 small meals a day. While trying to properly distribute carbs, protein, fruits, veggies, and dairy.
3rd rowi would say my ideal diet is my current diet
4th rowHealthy, fresh veggies/fruits & organic foods
5th rowIdeally I would like to be able to eat healthier foods in order to loose weight.
ValueCountFrequency (%)
and 90
 
5.0%
i 75
 
4.2%
to 72
 
4.0%
diet 56
 
3.1%
would 54
 
3.0%
my 50
 
2.8%
a 44
 
2.5%
eat 39
 
2.2%
of 37
 
2.1%
more 34
 
1.9%
Other values (429) 1240
69.2%
2025-04-10T14:13:30.578802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1738
18.0%
e 922
 
9.5%
t 716
 
7.4%
a 678
 
7.0%
o 660
 
6.8%
i 462
 
4.8%
s 451
 
4.7%
l 437
 
4.5%
n 419
 
4.3%
d 406
 
4.2%
Other values (49) 2770
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1738
18.0%
e 922
 
9.5%
t 716
 
7.4%
a 678
 
7.0%
o 660
 
6.8%
i 462
 
4.8%
s 451
 
4.7%
l 437
 
4.5%
n 419
 
4.3%
d 406
 
4.2%
Other values (49) 2770
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1738
18.0%
e 922
 
9.5%
t 716
 
7.4%
a 678
 
7.0%
o 660
 
6.8%
i 462
 
4.8%
s 451
 
4.7%
l 437
 
4.5%
n 419
 
4.3%
d 406
 
4.2%
Other values (49) 2770
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1738
18.0%
e 922
 
9.5%
t 716
 
7.4%
a 678
 
7.0%
o 660
 
6.8%
i 462
 
4.8%
s 451
 
4.7%
l 437
 
4.5%
n 419
 
4.3%
d 406
 
4.2%
Other values (49) 2770
28.7%

ideal_diet_coded
Real number (ℝ)

Distinct8
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.704
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:30.818833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0869178
Coefficient of variation (CV)0.56342273
Kurtosis-1.1654073
Mean3.704
Median Absolute Deviation (MAD)1
Skewness0.50478161
Sum463
Variance4.3552258
MonotonicityNot monotonic
2025-04-10T14:13:30.900352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 44
35.2%
3 17
 
13.6%
7 16
 
12.8%
5 15
 
12.0%
6 13
 
10.4%
1 11
 
8.8%
4 6
 
4.8%
8 3
 
2.4%
ValueCountFrequency (%)
1 11
 
8.8%
2 44
35.2%
3 17
 
13.6%
4 6
 
4.8%
5 15
 
12.0%
6 13
 
10.4%
7 16
 
12.8%
8 3
 
2.4%
ValueCountFrequency (%)
8 3
 
2.4%
7 16
 
12.8%
6 13
 
10.4%
5 15
 
12.0%
4 6
 
4.8%
3 17
 
13.6%
2 44
35.2%
1 11
 
8.8%

income
Real number (ℝ)

Distinct6
Distinct (%)4.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean4.5322581
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:30.978868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4563318
Coefficient of variation (CV)0.32132587
Kurtosis-0.21082518
Mean4.5322581
Median Absolute Deviation (MAD)1
Skewness-0.82734211
Sum562
Variance2.1209022
MonotonicityNot monotonic
2025-04-10T14:13:31.054871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 41
32.8%
5 33
26.4%
4 20
16.0%
3 17
13.6%
2 7
 
5.6%
1 6
 
4.8%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
1 6
 
4.8%
2 7
 
5.6%
3 17
13.6%
4 20
16.0%
5 33
26.4%
6 41
32.8%
ValueCountFrequency (%)
6 41
32.8%
5 33
26.4%
4 20
16.0%
3 17
13.6%
2 7
 
5.6%
1 6
 
4.8%

indian_food
Categorical

High correlation 

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
36 
3
31 
1
25 
2
18 
4
15 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row2

Common Values

ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Length

2025-04-10T14:13:31.138382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:31.218900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring characters

ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

italian_food
Categorical

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
100 
4
16 
3
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Length

2025-04-10T14:13:31.308421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:31.380939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring characters

ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

life_rewarding
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)8.1%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean5.1048387
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:31.453946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1203985
Coefficient of variation (CV)0.6112629
Kurtosis-1.4751391
Mean5.1048387
Median Absolute Deviation (MAD)3
Skewness0.062603108
Sum633
Variance9.736887
MonotonicityNot monotonic
2025-04-10T14:13:31.528454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 23
18.4%
8 18
14.4%
3 15
12.0%
7 14
11.2%
2 13
10.4%
9 11
8.8%
10 10
8.0%
5 10
8.0%
4 6
 
4.8%
6 4
 
3.2%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
1 23
18.4%
2 13
10.4%
3 15
12.0%
4 6
 
4.8%
5 10
8.0%
6 4
 
3.2%
7 14
11.2%
8 18
14.4%
9 11
8.8%
10 10
8.0%
ValueCountFrequency (%)
10 10
8.0%
9 11
8.8%
8 18
14.4%
7 14
11.2%
6 4
 
3.2%
5 10
8.0%
4 6
 
4.8%
3 15
12.0%
2 13
10.4%
1 23
18.4%

marital_status
Categorical

Distinct3
Distinct (%)2.4%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
64 
2.0
59 
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 64
51.2%
2.0 59
47.2%
4.0 1
 
0.8%
(Missing) 1
 
0.8%

Length

2025-04-10T14:13:31.609979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:31.682496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 64
51.6%
2.0 59
47.6%
4.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

meals_dinner_friend
Text

Missing 

Distinct121
Distinct (%)99.2%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
2025-04-10T14:13:31.853015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length145
Median length52
Mean length33.606557
Min length12

Characters and Unicode

Total characters4100
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)98.4%

Sample

1st rowrice, chicken, soup
2nd rowPasta, steak, chicken
3rd rowchicken and rice with veggies, pasta, some kind of healthy recipe
4th rowGrilled chicken Stuffed Shells Homemade Chili
5th rowChicken Parmesan, Pulled Pork, Spaghetti and meatballs
ValueCountFrequency (%)
chicken 80
 
13.1%
pasta 56
 
9.2%
and 39
 
6.4%
steak 39
 
6.4%
pizza 29
 
4.7%
rice 21
 
3.4%
spaghetti 18
 
2.9%
soup 8
 
1.3%
tacos 8
 
1.3%
with 7
 
1.1%
Other values (180) 306
50.1%
2025-04-10T14:13:32.166438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
533
13.0%
a 430
 
10.5%
e 360
 
8.8%
i 256
 
6.2%
t 233
 
5.7%
s 223
 
5.4%
, 218
 
5.3%
c 201
 
4.9%
n 197
 
4.8%
h 161
 
3.9%
Other values (44) 1288
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
533
13.0%
a 430
 
10.5%
e 360
 
8.8%
i 256
 
6.2%
t 233
 
5.7%
s 223
 
5.4%
, 218
 
5.3%
c 201
 
4.9%
n 197
 
4.8%
h 161
 
3.9%
Other values (44) 1288
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
533
13.0%
a 430
 
10.5%
e 360
 
8.8%
i 256
 
6.2%
t 233
 
5.7%
s 223
 
5.4%
, 218
 
5.3%
c 201
 
4.9%
n 197
 
4.8%
h 161
 
3.9%
Other values (44) 1288
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
533
13.0%
a 430
 
10.5%
e 360
 
8.8%
i 256
 
6.2%
t 233
 
5.7%
s 223
 
5.4%
, 218
 
5.3%
c 201
 
4.9%
n 197
 
4.8%
h 161
 
3.9%
Other values (44) 1288
31.4%

mother_education
Categorical

Missing 

Distinct5
Distinct (%)4.1%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
4.0
46 
2.0
30 
5.0
23 
3.0
18 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters366
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
4.0 46
36.8%
2.0 30
24.0%
5.0 23
18.4%
3.0 18
 
14.4%
1.0 5
 
4.0%
(Missing) 3
 
2.4%

Length

2025-04-10T14:13:32.270960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:32.353848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 46
37.7%
2.0 30
24.6%
5.0 23
18.9%
3.0 18
 
14.8%
1.0 5
 
4.1%

Most occurring characters

ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

mother_profession
Text

Missing 

Distinct112
Distinct (%)91.1%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2025-04-10T14:13:32.505613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length48
Median length31
Mean length14.813008
Min length2

Characters and Unicode

Total characters1822
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)83.7%

Sample

1st rowunemployed
2nd rowNurse RN
3rd rowowns business
4th rowSpecial Education Teacher
5th rowSubstance Abuse Conselor
ValueCountFrequency (%)
teacher 14
 
5.6%
secretary 7
 
2.8%
business 6
 
2.4%
nurse 5
 
2.0%
school 5
 
2.0%
unemployed 4
 
1.6%
home 4
 
1.6%
accountant 4
 
1.6%
in 4
 
1.6%
coordinator 3
 
1.2%
Other values (151) 192
77.4%
2025-04-10T14:13:32.781679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%
Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
4
43 
2
36 
3
20 
5
16 
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row4
4th row2
5th row3

Common Values

ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Length

2025-04-10T14:13:32.891194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:32.971710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring characters

ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

on_off_campus
Categorical

Distinct4
Distinct (%)3.2%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
97 
2.0
16 
3.0
 
9
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 97
77.6%
2.0 16
 
12.8%
3.0 9
 
7.2%
4.0 2
 
1.6%
(Missing) 1
 
0.8%

Length

2025-04-10T14:13:33.058712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:33.136238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 97
78.2%
2.0 16
 
12.9%
3.0 9
 
7.3%
4.0 2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

parents_cook
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
75 
2
36 
3
13 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Length

2025-04-10T14:13:33.218751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:33.295266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

pay_meal_out
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3
67 
4
22 
2
17 
5
11 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row3
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Length

2025-04-10T14:13:33.382786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:33.459789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring characters

ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

persian_food
Categorical

High correlation 

Distinct5
Distinct (%)4.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
30 
3.0
29 
2.0
26 
5.0
23 
4.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row5.0
4th row5.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 30
24.0%
3.0 29
23.2%
2.0 26
20.8%
5.0 23
18.4%
4.0 16
12.8%
(Missing) 1
 
0.8%

Length

2025-04-10T14:13:33.552303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:33.629821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 30
24.2%
3.0 29
23.4%
2.0 26
21.0%
5.0 23
18.5%
4.0 16
12.9%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

self_perception_weight
Real number (ℝ)

Distinct6
Distinct (%)4.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3.1209677
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:33.711338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1159796
Coefficient of variation (CV)0.35757485
Kurtosis0.2673123
Mean3.1209677
Median Absolute Deviation (MAD)1
Skewness0.47081775
Sum387
Variance1.2454104
MonotonicityNot monotonic
2025-04-10T14:13:33.787849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 45
36.0%
4 31
24.8%
2 31
24.8%
5 6
 
4.8%
1 6
 
4.8%
6 5
 
4.0%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
1 6
 
4.8%
2 31
24.8%
3 45
36.0%
4 31
24.8%
5 6
 
4.8%
6 5
 
4.0%
ValueCountFrequency (%)
6 5
 
4.0%
5 6
 
4.8%
4 31
24.8%
3 45
36.0%
2 31
24.8%
1 6
 
4.8%

soup
Categorical

Distinct2
Distinct (%)1.6%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
97 
2.0
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 97
77.6%
2.0 27
 
21.6%
(Missing) 1
 
0.8%

Length

2025-04-10T14:13:33.871362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:33.944364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 97
78.2%
2.0 27
 
21.8%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

sports
Categorical

Missing 

Distinct2
Distinct (%)1.6%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
1.0
75 
2.0
48 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 75
60.0%
2.0 48
38.4%
(Missing) 2
 
1.6%

Length

2025-04-10T14:13:34.021881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:34.092408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 75
61.0%
2.0 48
39.0%

Most occurring characters

ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

thai_food
Categorical

High correlation 

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
37 
3
26 
4
25 
1
20 
2
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Length

2025-04-10T14:13:34.169924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:34.254925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring characters

ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%
Distinct4
Distinct (%)3.2%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1165.0
46 
940.0
43 
725.0
22 
580.0
13 

Length

Max length6
Median length5
Mean length5.3709677
Min length5

Characters and Unicode

Total characters666
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1165.0
2nd row725.0
3rd row1165.0
4th row725.0
5th row940.0

Common Values

ValueCountFrequency (%)
1165.0 46
36.8%
940.0 43
34.4%
725.0 22
17.6%
580.0 13
 
10.4%
(Missing) 1
 
0.8%

Length

2025-04-10T14:13:34.347440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:34.423949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1165.0 46
37.1%
940.0 43
34.7%
725.0 22
17.7%
580.0 13
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

turkey_calories
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
500
50 
690
39 
345
26 
850
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row345
2nd row690
3rd row500
4th row690
5th row500

Common Values

ValueCountFrequency (%)
500 50
40.0%
690 39
31.2%
345 26
20.8%
850 10
 
8.0%

Length

2025-04-10T14:13:34.512470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:34.589994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
500 50
40.0%
690 39
31.2%
345 26
20.8%
850 10
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%
Distinct67
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2025-04-10T14:13:34.699466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length59
Median length38
Mean length10.344
Min length4

Characters and Unicode

Total characters1293
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)40.8%

Sample

1st rowcar racing
2nd rowBasketball
3rd rownone
4th rowUnknow
5th rowSoftball
ValueCountFrequency (%)
unknow 26
 
13.4%
hockey 15
 
7.7%
none 14
 
7.2%
soccer 11
 
5.7%
softball 10
 
5.2%
basketball 10
 
5.2%
volleyball 9
 
4.6%
and 5
 
2.6%
tennis 5
 
2.6%
lacrosse 5
 
2.6%
Other values (59) 84
43.3%
2025-04-10T14:13:34.926012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

veggies_day
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
53 
4
37 
3
21 
2
11 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row3
5th row4

Common Values

ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Length

2025-04-10T14:13:35.030158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:35.109671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

vitamins
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
64 
1
61 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Length

2025-04-10T14:13:35.201188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:35.271444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring characters

ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

waffle_calories
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1315
62 
900
38 
760
22 
575
 
3

Length

Max length4
Median length3
Mean length3.496
Min length3

Characters and Unicode

Total characters437
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1315
2nd row900
3rd row900
4th row1315
5th row760

Common Values

ValueCountFrequency (%)
1315 62
49.6%
900 38
30.4%
760 22
 
17.6%
575 3
 
2.4%

Length

2025-04-10T14:13:35.351459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T14:13:35.427963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1315 62
49.6%
900 38
30.4%
760 22
 
17.6%
575 3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

weight
Categorical

High correlation  Missing 

Distinct47
Distinct (%)38.2%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
135
 
8
140
 
8
150
 
7
170
 
7
155
 
6
Other values (42)
87 

Length

Max length24
Median length3
Mean length3.2845528
Min length3

Characters and Unicode

Total characters404
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)21.1%

Sample

1st row187
2nd row155
3rd rowI'm not answering this.
4th rowNot sure, 240
5th row190

Common Values

ValueCountFrequency (%)
135 8
 
6.4%
140 8
 
6.4%
150 7
 
5.6%
170 7
 
5.6%
155 6
 
4.8%
175 6
 
4.8%
180 6
 
4.8%
185 6
 
4.8%
190 5
 
4.0%
125 5
 
4.0%
Other values (37) 59
47.2%

Length

2025-04-10T14:13:35.514479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
135 8
 
6.2%
140 8
 
6.2%
150 7
 
5.4%
170 7
 
5.4%
155 6
 
4.7%
175 6
 
4.7%
180 6
 
4.7%
185 6
 
4.7%
165 5
 
3.9%
125 5
 
3.9%
Other values (42) 65
50.4%

Most occurring characters

ValueCountFrequency (%)
1 119
29.5%
0 61
15.1%
5 59
14.6%
2 28
 
6.9%
8 19
 
4.7%
7 18
 
4.5%
3 18
 
4.5%
4 17
 
4.2%
6 17
 
4.2%
9 10
 
2.5%
Other values (21) 38
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 119
29.5%
0 61
15.1%
5 59
14.6%
2 28
 
6.9%
8 19
 
4.7%
7 18
 
4.5%
3 18
 
4.5%
4 17
 
4.2%
6 17
 
4.2%
9 10
 
2.5%
Other values (21) 38
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 119
29.5%
0 61
15.1%
5 59
14.6%
2 28
 
6.9%
8 19
 
4.7%
7 18
 
4.5%
3 18
 
4.5%
4 17
 
4.2%
6 17
 
4.2%
9 10
 
2.5%
Other values (21) 38
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 119
29.5%
0 61
15.1%
5 59
14.6%
2 28
 
6.9%
8 19
 
4.7%
7 18
 
4.5%
3 18
 
4.5%
4 17
 
4.2%
6 17
 
4.2%
9 10
 
2.5%
Other values (21) 38
 
9.4%

Interactions

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2025-04-10T14:13:15.171180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.857818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.481669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.159808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.879317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.645485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.301936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.957476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.650551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:14.559261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.243192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.923823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.564189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.241934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.951321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.717956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.370872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.026477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.727627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:14.636260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.312698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.995337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.639189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.315950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.018830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.790479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.438033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.098997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.792949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:14.701778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.379226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.055850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-10T14:13:17.387161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.216441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.856940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.500063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.160194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.868469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:14.767298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.443230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.113850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.769231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.452164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.279957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.918939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.563585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.219194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.938465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:14.840304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.517756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.179368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.839231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.524631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.349968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.991458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.646595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.287715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.996982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:14.904833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.581269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.237368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.899747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.587148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.406479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.052465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.704902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.342719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:21.066500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:14.977350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.655787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.304879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.967214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.657671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.468966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.119968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.773422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.409226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:21.130500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.041635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.720787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-10T14:13:17.035212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.723672image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.530967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.180485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.835424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.466440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:21.191017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.105665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:15.787306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:16.418391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.096728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:17.804199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:18.586480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.241488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:19.893941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T14:13:20.522441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-04-10T14:13:35.750526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
GPAGenderbreakfastcalories_chickencalories_daycalories_sconecoffeecomfort_food_reasons_coded.1cookcuisinediet_current_codeddrinkeating_changes_codedeating_changes_coded1eating_outemploymentethnic_foodexercisefather_educationfav_cuisine_codedfav_foodfriesfruit_daygrade_levelgreek_foodhealthy_feelingideal_diet_codedincomeindian_fooditalian_foodlife_rewardingmarital_statusmother_educationnutritional_checkon_off_campusparents_cookpay_meal_outpersian_foodself_perception_weightsoupsportsthai_foodtortilla_caloriesturkey_caloriesveggies_dayvitaminswaffle_caloriesweight
GPA1.0000.0000.0000.1850.0000.1650.000-0.0690.000-0.0670.1150.1620.1260.0810.0840.0000.2200.0690.213-0.0280.0000.1910.3070.0710.102-0.077-0.1510.0830.0660.170-0.0050.3640.2380.2070.1380.0000.0770.0000.0200.0000.0000.1530.1510.1080.1730.0000.0000.068
Gender0.0001.0000.0540.0000.0000.1180.0000.1510.3290.0000.0640.1640.0770.0000.0650.1250.1400.1540.0000.1560.0780.0000.0640.0000.1600.2110.2900.0000.0000.0000.0000.1910.2050.1200.0690.0680.1150.0000.2400.0000.1550.1470.1630.2820.1000.0000.0000.421
breakfast0.0000.0541.0000.0000.0000.0000.0000.1760.1000.0000.1860.1660.1230.1060.0000.0000.3220.0000.1900.2410.1900.0000.2990.1280.2580.0000.0000.1610.1900.0000.1100.2190.0000.0810.1090.0000.0000.0000.0000.1220.0000.0000.0000.0330.2950.0000.0000.000
calories_chicken0.1850.0000.0001.0000.2050.1410.2040.0000.0000.0370.0570.0150.0000.1090.0000.0000.1070.0590.1980.2140.0000.0000.0000.1910.0240.0870.1180.0000.0000.1920.1400.1890.1630.1060.0000.0840.0000.0000.0000.0000.0630.0320.2540.2330.0000.1400.2240.042
calories_day0.0000.0000.0000.2051.0000.1260.0280.1470.0550.0000.1000.0000.0000.1640.0670.0000.1590.2000.0190.0000.0780.0000.1640.0000.0990.1530.2370.0000.0970.0000.0000.0000.0000.2690.0000.0000.0000.0000.1510.0000.0790.1350.1170.1680.1070.3090.1300.000
calories_scone0.1650.1180.0000.1410.1261.0000.0560.2270.1190.1740.0750.0400.0000.0000.1540.1050.0900.0000.2770.2080.0000.0000.0430.0000.0000.1560.0000.0170.1260.1320.1240.0000.0000.2620.1150.1870.1430.0000.2810.0000.0000.1110.1810.1030.1230.0000.2740.000
coffee0.0000.0000.0000.2040.0280.0561.0000.1570.0990.0000.0000.0000.0000.1700.1330.0000.1470.0000.0000.2110.0000.0000.0000.1150.0000.0000.1410.0000.0000.0960.1300.1330.1260.2260.0000.0800.0000.0000.0000.1440.0000.1060.0000.0000.0770.0000.0000.000
comfort_food_reasons_coded.1-0.0690.1510.1760.0000.1470.2270.1571.0000.1320.1430.1720.1640.000-0.0310.0000.0810.0000.1620.157-0.0210.1250.1350.1290.0490.000-0.0640.042-0.0800.0580.0000.0440.2660.2090.1820.0000.0000.0000.173-0.0900.1280.0830.0000.0210.0560.1750.0000.0000.210
cook0.0000.3290.1000.0000.0550.1190.0990.1321.0000.0000.1680.1040.0000.0000.0340.0000.0920.1610.1370.0000.0000.1590.0000.1430.0000.0000.1700.0000.0000.1260.0730.0000.0790.1660.0790.1540.0920.0000.0000.0000.1380.0370.1060.1700.0000.0550.2230.187
cuisine-0.0670.0000.0000.0370.0000.1740.0000.1430.0001.0000.0870.1330.0000.1100.0000.0000.0000.0730.1790.0010.1350.2280.0000.0380.0000.1330.088-0.1040.0760.2080.0980.3840.1900.0000.0000.0620.1440.1020.1380.0000.0990.0660.0780.0000.0960.0000.2430.307
diet_current_coded0.1150.0640.1860.0570.1000.0750.0000.1720.1680.0871.0000.3050.1980.2580.0300.0000.0000.0780.0450.0000.0000.1540.1030.0000.0000.1000.1260.2130.0000.1100.1090.2950.0000.1970.1670.0000.0010.0320.0000.0000.0120.0470.0730.0000.2160.1380.1570.000
drink0.1620.1640.1660.0150.0000.0400.0000.1640.1040.1330.3051.0000.1890.1310.0000.1150.1290.0000.0000.0000.0220.1910.0810.0000.0610.0000.0000.2430.1720.0000.1160.0000.1630.2090.0000.0000.0140.2700.1010.0000.0000.0000.0000.0000.2520.1260.0000.260
eating_changes_coded0.1260.0770.1230.0000.0000.0000.0000.0000.0000.0000.1980.1891.0000.7840.0000.1590.1080.0000.1370.0000.0000.0000.0000.0000.0740.0000.0000.0000.1330.1780.0000.1810.0990.1520.1230.0000.1000.0000.0000.0000.0000.0680.1210.0000.0000.0860.0000.375
eating_changes_coded10.0810.0000.1060.1090.1640.0000.170-0.0310.0000.1100.2580.1310.7841.0000.0800.0550.0730.0000.0000.0940.0000.1210.0720.0000.037-0.030-0.021-0.0090.1380.0420.0180.0000.0880.1360.0230.0000.0220.119-0.0430.1960.0000.0670.0000.0000.0000.1830.0000.134
eating_out0.0840.0650.0000.0000.0670.1540.1330.0000.0340.0000.0300.0000.0000.0801.0000.0000.0000.0000.2270.0270.0940.0790.0000.1030.0000.1090.0000.0000.0000.0000.0900.1120.0970.0000.0000.1610.0560.1150.0000.0000.0000.0000.0000.0930.0000.1160.1160.068
employment0.0000.1250.0000.0000.0000.1050.0000.0810.0000.0000.0000.1150.1590.0550.0001.0000.0000.0000.1140.0000.0000.0420.0000.0000.0520.0000.0600.1150.0000.0000.0000.0000.0980.0730.3430.0000.0000.0000.1700.0000.1410.1030.1050.0000.0000.0000.0000.551
ethnic_food0.2200.1400.3220.1070.1590.0900.1470.0000.0920.0000.0000.1290.1080.0730.0000.0001.0000.0000.0890.2560.2440.0350.2580.0000.3320.0000.0000.0000.4640.1700.1430.0000.0000.1540.0000.0730.1050.3650.0000.2640.0000.3950.0000.0490.1660.0660.0570.000
exercise0.0690.1540.0000.0590.2000.0000.0000.1620.1610.0730.0780.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.2010.0700.0000.2020.0000.0000.0000.1070.0000.0000.0000.1190.1190.0000.0000.0000.3080.0000.4180.0000.0750.0690.1030.0000.0000.217
father_education0.2130.0000.1900.1980.0190.2770.0000.1570.1370.1790.0450.0000.1370.0000.2270.1140.0890.0001.0000.3260.0260.0360.1590.0810.1060.1440.0900.2740.1340.2560.1540.3210.3060.1020.1390.0920.1860.1390.0000.0000.1140.0000.1200.0830.1350.1710.0850.242
fav_cuisine_coded-0.0280.1560.2410.2140.0000.2080.211-0.0210.0000.0010.0000.0000.0000.0940.0270.0000.2560.0000.3261.0000.1150.3430.1880.0000.160-0.043-0.046-0.2860.2060.3490.0310.2090.2460.0340.0000.1670.2230.1820.0550.0000.0000.2110.1620.0000.2490.1010.1250.157
fav_food0.0000.0780.1900.0000.0780.0000.0000.1250.0000.1350.0000.0220.0000.0000.0940.0000.2440.0000.0260.1151.0000.0850.1190.0000.2540.0000.0000.0000.1270.0930.0610.1500.1690.0000.0810.1090.1390.0760.0890.2530.0630.0950.0000.1310.1270.0000.0000.159
fries0.1910.0000.0000.0000.0000.0000.0000.1350.1590.2280.1540.1910.0000.1210.0790.0420.0350.0000.0360.3430.0851.0000.0000.0550.1020.0000.0000.0000.1520.0000.1520.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.2770.1420.229
fruit_day0.3070.0640.2990.0000.1640.0430.0000.1290.0000.0000.1030.0810.0000.0720.0000.0000.2580.2010.1590.1880.1190.0001.0000.0970.1260.1710.0000.0000.1240.0000.1110.0000.0790.1460.0000.0000.0000.0140.1650.1650.0000.0240.0000.0790.4260.2470.0000.000
grade_level0.0710.0000.1280.1910.0000.0000.1150.0490.1430.0380.0000.0000.0000.0000.1030.0000.0000.0700.0810.0000.0000.0550.0971.0000.1340.0000.0800.1710.0450.1650.0000.0580.0910.1360.2610.0950.0000.1070.1400.0000.2150.2130.0000.0330.0670.0000.0000.141
greek_food0.1020.1600.2580.0240.0990.0000.0000.0000.0000.0000.0000.0610.0740.0370.0000.0520.3320.0000.1060.1600.2540.1020.1260.1341.0000.0000.0000.0000.3670.1650.0000.0000.1050.1080.0000.0000.0000.4140.0000.0000.0340.3210.0000.0000.1030.0000.0800.000
healthy_feeling-0.0770.2110.0000.0870.1530.1560.000-0.0640.0000.1330.1000.0000.000-0.0300.1090.0000.0000.2020.144-0.0430.0000.0000.1710.0000.0001.000-0.0340.0140.0000.0000.6260.0000.0000.1630.0000.1450.1020.0000.0160.0000.2980.0710.0000.0000.1620.0520.0000.168
ideal_diet_coded-0.1510.2900.0000.1180.2370.0000.1410.0420.1700.0880.1260.0000.000-0.0210.0000.0600.0000.0000.090-0.0460.0000.0000.0000.0800.000-0.0341.0000.0820.0000.000-0.0040.3740.2370.0460.0000.0810.0000.000-0.2830.0000.1600.0000.0000.0630.1630.0000.0000.147
income0.0830.0000.1610.0000.0000.0170.000-0.0800.000-0.1040.2130.2430.000-0.0090.0000.1150.0000.0000.274-0.2860.0000.0000.0000.1710.0000.0140.0821.0000.0520.0850.0380.0000.1860.0720.0000.0000.0310.000-0.1470.1510.1480.0000.0000.1060.0300.0000.1220.124
indian_food0.0660.0000.1900.0000.0970.1260.0000.0580.0000.0760.0000.1720.1330.1380.0000.0000.4640.0000.1340.2060.1270.1520.1240.0450.3670.0000.0000.0521.0000.0300.1070.0000.1510.0000.0940.0000.0000.5470.0000.0670.0000.5570.0000.0000.0000.0000.1260.062
italian_food0.1700.0000.0000.1920.0000.1320.0960.0000.1260.2080.1100.0000.1780.0420.0000.0000.1700.1070.2560.3490.0930.0000.0000.1650.1650.0000.0000.0850.0301.0000.0680.2720.2270.0000.0000.0000.0000.0590.1790.0800.2300.1640.1430.0000.1000.1700.1390.180
life_rewarding-0.0050.0000.1100.1400.0000.1240.1300.0440.0730.0980.1090.1160.0000.0180.0900.0000.1430.0000.1540.0310.0610.1520.1110.0000.0000.626-0.0040.0380.1070.0681.0000.2180.0520.0880.0000.0000.0000.104-0.1280.0000.0000.0000.0000.0000.1400.0000.0610.000
marital_status0.3640.1910.2190.1890.0000.0000.1330.2660.0000.3840.2950.0000.1810.0000.1120.0000.0000.0000.3210.2090.1500.0000.0000.0580.0000.0000.3740.0000.0000.2720.2181.0000.3020.0000.0620.1700.1500.0000.0000.1140.0770.0000.1310.0000.0290.1820.0410.000
mother_education0.2380.2050.0000.1630.0000.0000.1260.2090.0790.1900.0000.1630.0990.0880.0970.0980.0000.0000.3060.2460.1690.0000.0790.0910.1050.0000.2370.1860.1510.2270.0520.3021.0000.0000.0000.0000.1230.1760.0000.0000.0000.0000.0000.0860.0790.0000.0000.244
nutritional_check0.2070.1200.0810.1060.2690.2620.2260.1820.1660.0000.1970.2090.1520.1360.0000.0730.1540.1190.1020.0340.0000.0000.1460.1360.1080.1630.0460.0720.0000.0000.0880.0000.0001.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.2060.3740.0000.000
on_off_campus0.1380.0690.1090.0000.0000.1150.0000.0000.0790.0000.1670.0000.1230.0230.0000.3430.0000.1190.1390.0000.0810.0000.0000.2610.0000.0000.0000.0000.0940.0000.0000.0620.0000.0001.0000.0680.0000.0710.0560.0000.0000.0630.0000.0920.0000.0000.0000.377
parents_cook0.0000.0680.0000.0840.0000.1870.0800.0000.1540.0620.0000.0000.0000.0000.1610.0000.0730.0000.0920.1670.1090.0000.0000.0950.0000.1450.0810.0000.0000.0000.0000.1700.0000.0000.0681.0000.1170.0000.0000.0000.1640.0000.1590.0390.0000.0000.0440.000
pay_meal_out0.0770.1150.0000.0000.0000.1430.0000.0000.0920.1440.0010.0140.1000.0220.0560.0000.1050.0000.1860.2230.1390.1070.0000.0000.0000.1020.0000.0310.0000.0000.0000.1500.1230.1040.0000.1171.0000.0000.0000.0000.0000.0950.0000.0000.1430.1930.0000.182
persian_food0.0000.0000.0000.0000.0000.0000.0000.1730.0000.1020.0320.2700.0000.1190.1150.0000.3650.0000.1390.1820.0760.0000.0140.1070.4140.0000.0000.0000.5470.0590.1040.0000.1760.0000.0710.0000.0001.0000.0000.1310.0000.3950.1070.0000.0000.0000.0000.174
self_perception_weight0.0200.2400.0000.0000.1510.2810.000-0.0900.0000.1380.0000.1010.000-0.0430.0000.1700.0000.3080.0000.0550.0890.0000.1650.1400.0000.016-0.283-0.1470.0000.179-0.1280.0000.0000.0000.0560.0000.0000.0001.0000.0000.2970.0000.0730.0000.0960.0000.0730.292
soup0.0000.0000.1220.0000.0000.0000.1440.1280.0000.0000.0000.0000.0000.1960.0000.0000.2640.0000.0000.0000.2530.0000.1650.0000.0000.0000.0000.1510.0670.0800.0000.1140.0000.0000.0000.0000.0000.1310.0001.0000.0000.0000.0990.0000.1170.0000.0000.291
sports0.0000.1550.0000.0630.0790.0000.0000.0830.1380.0990.0120.0000.0000.0000.0000.1410.0000.4180.1140.0000.0630.0000.0000.2150.0340.2980.1600.1480.0000.2300.0000.0770.0000.0000.0000.1640.0000.0000.2970.0001.0000.0790.1430.1460.0000.0000.0000.387
thai_food0.1530.1470.0000.0320.1350.1110.1060.0000.0370.0660.0470.0000.0680.0670.0000.1030.3950.0000.0000.2110.0950.0000.0240.2130.3210.0710.0000.0000.5570.1640.0000.0000.0000.0000.0630.0000.0950.3950.0000.0000.0791.0000.0770.0680.0000.0000.0300.226
tortilla_calories0.1510.1630.0000.2540.1170.1810.0000.0210.1060.0780.0730.0000.1210.0000.0000.1050.0000.0750.1200.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.1430.0000.1310.0000.0000.0000.1590.0000.1070.0730.0990.1430.0771.0000.2550.0930.0000.2280.000
turkey_calories0.1080.2820.0330.2330.1680.1030.0000.0560.1700.0000.0000.0000.0000.0000.0930.0000.0490.0690.0830.0000.1310.0000.0790.0330.0000.0000.0630.1060.0000.0000.0000.0000.0860.0000.0920.0390.0000.0000.0000.0000.1460.0680.2551.0000.0790.0000.2130.000
veggies_day0.1730.1000.2950.0000.1070.1230.0770.1750.0000.0960.2160.2520.0000.0000.0000.0000.1660.1030.1350.2490.1270.0000.4260.0670.1030.1620.1630.0300.0000.1000.1400.0290.0790.2060.0000.0000.1430.0000.0960.1170.0000.0000.0930.0791.0000.2330.1020.000
vitamins0.0000.0000.0000.1400.3090.0000.0000.0000.0550.0000.1380.1260.0860.1830.1160.0000.0660.0000.1710.1010.0000.2770.2470.0000.0000.0520.0000.0000.0000.1700.0000.1820.0000.3740.0000.0000.1930.0000.0000.0000.0000.0000.0000.0000.2331.0000.1650.142
waffle_calories0.0000.0000.0000.2240.1300.2740.0000.0000.2230.2430.1570.0000.0000.0000.1160.0000.0570.0000.0850.1250.0000.1420.0000.0000.0800.0000.0000.1220.1260.1390.0610.0410.0000.0000.0000.0440.0000.0000.0730.0000.0000.0300.2280.2130.1020.1651.0000.188
weight0.0680.4210.0000.0420.0000.0000.0000.2100.1870.3070.0000.2600.3750.1340.0680.5510.0000.2170.2420.1570.1590.2290.0000.1410.0000.1680.1470.1240.0620.1800.0000.0000.2440.0000.3770.0000.1820.1740.2920.2910.3870.2260.0000.0000.0000.1420.1881.000

Missing values

2025-04-10T14:13:21.353540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-10T14:13:21.752548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-10T14:13:22.125440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GPAGenderbreakfastcalories_chickencalories_daycalories_sconecoffeecomfort_foodcomfort_food_reasonscookcomfort_food_reasons_coded.1cuisinediet_currentdiet_current_codeddrinkeating_changeseating_changes_codedeating_changes_coded1eating_outemploymentethnic_foodexercisefather_educationfather_professionfav_cuisinefav_cuisine_codedfav_foodfood_childhoodfriesfruit_daygrade_levelgreek_foodhealthy_feelinghealthy_mealideal_dietideal_diet_codedincomeindian_fooditalian_foodlife_rewardingmarital_statusmeals_dinner_friendmother_educationmother_professionnutritional_checkon_off_campusparents_cookpay_meal_outpersian_foodself_perception_weightsoupsportsthai_foodtortilla_caloriesturkey_caloriestype_sportsveggies_dayvitaminswaffle_caloriesweight
02.400214300.0315.01nonewe dont have comfort2.096.0eat good and exercise11.0eat faster1133.011.05.0profesorArabic cuisine31.0rice and chicken25252looks not oilybeing healthy85.0551.01.0rice, chicken, soup1.0unemployed51.0125.03.01.01.011165.0345car racing511315187
13.654116103.0420.02chocolate, chips, ice creamStress, bored, anger3.011.0I eat about three times a day with some snacks. I try to eat healthy but it doesn't always work out that- sometimes eat fast food and mainly eat at Laker/ Egan22.0I eat out more than usual.1222.041.02.0Self employedItalian11.0chicken and biscuits, beef soup, baked beans14445Grains, Veggies, (more of grains and veggies), small protein and fruit with dairyTry to eat 5-6 small meals a day. While trying to properly distribute carbs, protein, fruits, veggies, and dairy.34.0441.02.0Pasta, steak, chicken4.0Nurse RN41.0144.03.01.01.02725.0690Basketball42900155
23.300117204.0420.02frozen yogurt, pizza, fast foodstress, sadness1.013.0toast and fruit for breakfast, salad for lunch, usually grilled chicken and veggies (or some variation) for dinner31.0sometimes choosing to eat fast food instead of cooking simply for convenience1323.052.02.0owns businessitalian13.0mac and cheese, pizza, tacos15356usually includes natural ingredients; nonprocessed foodi would say my ideal diet is my current diet66.0557.02.0chicken and rice with veggies, pasta, some kind of healthy recipe2.0owns business42.0135.06.01.02.051165.0500none51900I'm not answering this.
33.200114303.0420.02Pizza, Mac and cheese, ice creamBoredom2.022.0College diet, cheap and easy foods most nights. Weekends traditionally, cook better homemade meals22.0Accepting cheap and premade/store bought foods1323.053.02.0MechanicTurkish31.0Beef stroganoff, tacos, pizza24457Fresh fruits& vegetables, organic meatsHealthy, fresh veggies/fruits & organic foods26.0552.02.0Grilled chicken \rStuffed Shells\rHomemade Chili4.0Special Education Teacher21.0125.05.01.02.05725.0690Unknow311315Not sure, 240
43.500117202.0420.02Ice cream, chocolate, chipsStress, boredom, cravings1.012.0I try to eat healthy but often struggle because of living on campus. I still try to keep the choices I do make balanced with fruits and vegetables and limit the sweats.22.0I have eaten generally the same foods but I do find myself eating the same food frequently due to what I have found I like from egan and the laker.3422.041.04.0ITItalian13.0Pasta, chicken tender, pizza14446A lean protein such as grilled chicken, green vegetables and brown rice or other whole grainIdeally I would like to be able to eat healthier foods in order to loose weight.26.0251.01.0Chicken Parmesan, Pulled Pork, Spaghetti and meatballs5.0Substance Abuse Conselor31.0142.04.01.01.04940.0500Softball42760190
52.250116103.0980.02Candy, brownies and soda.None, i don't eat comfort food. I just eat when i'm hungry.3.046.0My current diet is terrible. I barely have time to eat a meal in a day. When i do eat it's mostly not healthy.22.0Eating rice everyday. Eating less homemade food.1313.042.01.0Taxi DriverAfrican63.0Fries, plaintain & fried fish12224Requires veggies, fruits and a cooked meal.My ideal diet is to eat 3 times a day including breakfast on time. Eat healthy food.21.0554.02.0Anything they'd want. I'd ask them before hand what they want to eat and it depends on which type of friend is coming.1.0Hair Braider11.0255.05.01.02.04940.0345None.121315190
63.800216103.0420.02Chocolate, ice cream, french fries, pretzelsstress, boredom2.011.0I eat a lot of chicken and broccoli for dinner, and usually tuna sandwiches for lunch.31.0I started eating a lot less and healthier because I wasn't playing sports year round anymore.2523.051.04.0AssemblerThai41.0grilled chicken, hamburgers14454Protein, vegetables, fruit, and some carbsI would ideally like to eat more fresh fruits and vegetables. However, its difficult to get to the store all the time to buy fresh.24.0558.01.0Grilled chicken, steak, pizza4.0Journalist42.0225.04.01.01.05940.0690soccer411315180
73.300117203.0420.01Ice cream, cheeseburgers, chips.I eat comfort food when im stressed out from school(finals week), when I`m sad, or when i am dealing with personal family issues.3.011.0I eat a very healthy diet. Ocassionally, i will eat out and get unhealthy food.12.0Freshmen year i ate very unhealthy, but now it is much healthier because of self control.2522.022.03.0Business guyAnything american style.51.0chicken, cheesey potatoes, and hot dogs15233A healthy meal has a piece of meat followed by a lot of fruit and veggiesMy ideal diet is filled with a lot of fruit and chicken. I also really enjoy eggs any type of way with toast.25.0133.01.0chicken, steak, pasta2.0cook41.0151.03.01.02.01725.0500none421315137
83.300114300.0420.01Donuts, ice cream, chipsBoredom3.021.0I eat whatever I want in moderation.11.0I snack less2852.050.05.0High School PrincipalSeafood13.0Shrimp, spaghetti14157ColorfulThe same as it is now.65.0558.02.0Pasta, Fish, Steak5.0Elementary School Teacher21.0235.04.02.02.05725.0345none32760180
93.300114303.0315.02Mac and cheese, chocolate, and pastaStress, anger and sadness3.011.0I eat healthy all the time when possible. I treat myself occasionally. I don't really like the greasy meals, if anything I would eat sweets over the greasy meals.11.0I cook a lot of my own foods back at home so not being able to cook my own healthy choices. I eat more carbs than normal when I'm at college due to the choices given in the cafe.1333.051.05.0commissioner of erie countyItalian11.0Pasta, Eggs, Pancakes15153Chicken and rice with a side of veggies.Lots of protein, carbs, and fruits and veggies.24.0453.02.0pasta salad and bread5.0Pharmaceutical rep51.0334.03.01.01.04580.0345field hockey51900125
GPAGenderbreakfastcalories_chickencalories_daycalories_sconecoffeecomfort_foodcomfort_food_reasonscookcomfort_food_reasons_coded.1cuisinediet_currentdiet_current_codeddrinkeating_changeseating_changes_codedeating_changes_coded1eating_outemploymentethnic_foodexercisefather_educationfather_professionfav_cuisinefav_cuisine_codedfav_foodfood_childhoodfriesfruit_daygrade_levelgreek_foodhealthy_feelinghealthy_mealideal_dietideal_diet_codedincomeindian_fooditalian_foodlife_rewardingmarital_statusmeals_dinner_friendmother_educationmother_professionnutritional_checkon_off_campusparents_cookpay_meal_outpersian_foodself_perception_weightsoupsportsthai_foodtortilla_caloriesturkey_caloriestype_sportsveggies_dayvitaminswaffle_caloriesweight
1153.300216104.0980.02chocolate bar, ice cream, pretzels, potato chips and protein bars.Stress, boredom and physical activity4.011.0I currently eat an abundance of carbohydrates. I have a low intake of protein. I overly intake calcium.22.0Eating more dairy due to more ice cream intake.1322.021.05.0PharmaceuticalI do not like cuisine03.0Pasta, Pizza, Popcorn151110Intaking the proper amount of each food groupMy ideal diet would be an equal balance of all the food groups. I would like to eat more protein. Also, i would prefer to cut back on the junk food.36.01410.02.0Pasta, Chicken, Pizza5.0Health teacher11.0152.02.01.01.011165.0690Hockey221315150
1163.400116100.0420.02Ice cream, chocolate, pizza, cucumberloneliness, homework, boredom3.026.0It is very unbalance. Mostly fat food, Lack of vegetables.21.0less vegetable more sweats1343.042.03.0Business ManChinese42.0Fry Chicken, Rice Vegetable15115BBQ Chicken with mash sweat potatoes and steam vegetable with corn and a glass of water.Very healthy. Freshly done. properly cooked51.0352.01.0Rice and Peas and Chicken, Jerk Chicken and Shrimp3.0Business Woman21.0231.04.01.02.05725.0345none511315170
1173.770116100.0315.02Noodle ( any kinds of noodle), Tuna sandwich, and Egg.\rWhen i'm eating with my close friends/ Food smell or look good/ when I feel tired3.054.0I eat in dining hall of school everyday. I usually have both meat and a little vegetable in every meal.11.0I eat more vegetable. Since coming to college, I started to eat salads and tried to eat salads at least three times a week.2522.040.02.0His own businessVietnamese cuisine43.0Noodle, Wings, and Tiramisu13229Including both vegetable and meatMy ideal diet should include both vegetable and meat. It is not only healthy but also delicious.22.0247.01.0Vietnamese fried rolls, Pho, Some kinds of noodles.2.0Her own business21.0122.04.01.02.05725.0690No, I don't play sport.31760113
1183.630114303.0420.01Chinese, chips, cakeStress and boredom3.011.0Try to eat as healthy as possible. A few days where fast food comes into play because of classes.21.0I try to eat more fruits and vegetables2522.042.02.0HVAC technicianAmerican51.0Chinese15235Chicken vegetables and fruit for dinnerAll home cooked. Healthy. Vegetables and fruit.53.0358.02.0Chinese tacos or pasta2.0Grieveance coordinator of the SCI albion prison23.0342.04.01.02.04940.0345Unknow521315140
1193.200216103.0420.02chips, rice, chicken curry,Happiness, boredom, social event2.075.0My diet is mostly whatever I get to eat in the grotto commons. Sometimes, I eat outside21.0Started eating a lot of protein rich food that i didnt before.2522.052.05.0United NationsIndian83.0pizza, burger, pasta15457A diet that is well balanced in most of the nutrients needed for the body.Healthy balanced diet that tastes well.32.0556.02.0Chicken, Rice, Vegetables5.0Banker21.0135.04.01.01.051165.0690Soccer521315185
1203.500116104.0420.02wine. mac and cheese, pizza, ice creamboredom and sadness3.021.0My diet consists mainly of coffee, water, fruits, vegetables, and chicken. I tend to stay away from bread and pasta as much as possible.22.0I have noticed there is less time for a prepared meal, so quick and easy has become the norm.1321.042.04.0AccountantItalian11.0Stromboli Mac and Cheese and Pizza15455mainly protein and vegetables with a complex carbMy ideal diet would consist of a majority of what I consume now. I like to think I make pretty healthy choices currently, so it would most likely remain the same.64.0357.01.0pasta, fish, steak3.0Radiological Technician53.0143.04.01.01.05940.0500Softball511315156
1213.000112652.0315.02Pizza / Wings / CheesecakeLoneliness / Homesick / Sadness3.036.0A college student with an imbalanced diet trying to be healthy.21.0Eating Pizza as an excuse when there is nothing else to it.1343.032.05.0DoctorMexican Food21.0Isombe , Plantains and Ugali14415A healthy meal is a variety of food , organic food that gives you the nutrients such as protein , carbohydrates , fat , water , vitamins and minerals.Eating home cooked meals everyday and being able to not eat processed foods at all.52.0557.01.0Fried Rice \rBaked potatoes \rCurry Chicken2.0Public Health Advisor31.0341.04.01.0NaN4940.0500basketball521315180
1223.882117200.0420.01rice, potato, seaweed soupsadness3.036.0Rice, oatmeal, and tea21.0less rice1333.052.05.0CEO of companyKorean41.0Rice and potato14356lots of vegetableslots of veggies22.05310.01.0meat, rice, kimchi1.0Real Estate manageer31.0245.04.01.02.05580.0690none421315120
1233.000217204.0420.01Mac n Cheese, Lasagna, Pizzahappiness, they are some of my favorite foods3.071.0I try to eat as healthy as possible everyday. I have fruit, yogurt and a protein shake everyday. The other foods i eat vary on a day to day basis.12.0I don't eat as much on a daily basis since coming to college.1852.021.03.0Store manager at Giant EagleItalian13.0pizza and spaghetti15111A protein, a fruit, a starch, and a salad or some sort of vegetable.My ideal diet is the diet i am currently on. The only thing i would change is a little bit less snack/junk food.64.0151.01.0Pizza, Spaghetti, Baked Ziti2.0Receptionist for a medical supply company41.0231.02.02.02.01940.0500Unknow311315135
1243.900114300.0315.02Chocolates, pizza, and Ritz.hormones, Premenstrual syndrome.NaN53.0high in protein and low in carbohydrates.11.0I have learned to eat more vegetables.2512.032.04.0JournalistHISPANIC CUISINE.21.0rice, beans, and chicken / pizza/ tenders13323a cup of rice, vegetables, and meat.Being able to balance between sweets, vegetables, fruits, carbohydrates, and fat.35.0235.02.0Vegetables, Meat, and rice.3.0House-wife51.0332.03.01.02.02725.0345Unknow42575135